Entries matching “review”

@EtienneLescot

@EtienneLescot

A desktop app for Muse Code on macOS and Windows with projects, git worktrees, subagents, MCP, a diff review panel and opt-in computer use that lets the agent see the screen and use apps.

@seb-patron

@seb-patron

Adversarial-review and fix-verification skills for Muse, built after two round-1 reviewers approved a diff with 0 blocking findings while an independent review found 13 real issues.

AICodeKing reviews Muse Glimmer for local agent setups, finding it strong at tool calling, multi-step tasks and failure recovery but weaker on general benchmarks.

@GGulati

@GGulati

A personal software factory built on Muse agents and Erlang/OTP principles: a markdown backlog goes in, and each run takes one item through planning, dual review, TDD, code review and verification.

@stevysmith

@stevysmith

An MCP server and skill that reports live review times for AI app, connector and plugin stores, including Meta Muse, so builders can see how long a connector submission is taking.

Pietro Zullo walks through submitting a hosted MCP server to the Muse connector platform: the three-step form (overview, technical specs, review) and what Meta's functional and security review checks.

Ahmed Yasser

@AhmedYsrrr

I just told @Muse - Find me a good barber in my city - Under $50 - Available Sunday at 11:30AM - Check Google reviews + Reddit - I don’t like fades, I usually get a crew cut It found one, picked the best reviewed barber, and booked the appointment, all without me touching

+1

I've been using Muse pretty heavily since shortly after it launched, and I'm curious what everyone else is actually doing with it. I've been trying to get past the "ask it a question" stage and use it more like a real personal assistant. So far I've been testing it for things like: - Creating a personalized morning briefing around the news and information I care about - Travel research, bookings and itineraries - Keeping track of projects - Reviewing bills and subscriptions - Researching things and then actually helping me follow through on them The biggest difference I'm noticing is that I'm starting to think less about "what question should I ask AI?" and more about "what do I need to get done?" I'm still figuring out where Muse is genuinely useful versus where it's just AI novelty. For those of you who have been using it for a few days: What's the most useful thing you've actually had Muse DO for you so far? And is there anything you're trying to make it do but haven't figured out yet? I've been testing different workflows almost every day, so I'd be interested in comparing notes. But probably my biggest Muse project so far has been putting together a Muse Tips & T

GitHub

@github

GitHub 🤝 @Muse A new way to connect your GitHub account to review PRs, catch up on issues and notifications, and leave comments without switching tabs.

@gregmos

@gregmos

Routing overlay for the Superpowers plugin for Claude Code where Muse writes the code, Codex reviews it, and rules keep expensive models from being misused.

@CBannink

@CBannink

Installer that routes OpenCode's coder and reviewer agents at a Muse coding subscription, calling api.meta.ai directly with no local proxy, reusing the muse login key.

@Sighton-GH

@Sighton-GH

opencode-delegate is a Claude Code plugin where Claude plans and writes briefs while opencode sessions on free models, Muse Spark 1.3 by default, implement and review each task.

@danny-hines

@danny-hines

Call Muse Code from Codex or ChatGPT desktop via MCP tools and skills for independent code review, approach comparison or delegated implementation, using a Muse Code subscription or API key.

@naileditnorthshore-lab

@naileditnorthshore-lab

Change Verifier is a Muse connector and MCP server that runs seven checks on vendor bank-account or payee changes, returns clear, review or block, and holds the change until a named owner accepts it.

@troioi-vn

@troioi-vn

delegate-to-muse is a Claude Code skill for handing repository work to Muse Code via muse exec, with a handoff template and checks for reviewing the diff and re-running tests rather than trusting the summary.

Federico Ramirez uses Muse Code and Spark 1.3 for GitHub issue-to-PR workflows. He finds output readable and $15/month good for 2-4 hours of continuous coding, but skill execution inconsistent and the sandbox too restrictive.

DesignRush argued Muse inserts itself into product discovery and ranks on structured data like specs and reviews rather than ad spend, and told brands to map the agent shopping journey, measure agent referrals separately, and audit who can access their product data.

CellCog explains the Muse connector platform: a three-step describe/review/list flow at muse.ai/platform, directory vs custom connectors, and Stripe Link payments.

OK, for context, I have Claude Code desktop app driving the CLI and orchestrating the code and verification tasks to try to save tokens. So Claude runs things, a Deepseek 4.1 Flash (cloud) session does the planning, Qwen 3.8 27b Q8 does the boiler plate coding and Muse Glimmer sanity checks the code and pushes any issues back to Qwen. If there are issues Qwen and Glimmer can't agree on, Deepseek validates. If Deepseek can't sort it out, it goes back to Claude. This had been working fine, but then in the last few days token use spiked, tasks that used to take 10 minutes were taking an hour or more and Qwen started going into more and more reasoning loops. It seems that since I last checked (on 12th September) the CLI changed. I used to strip unnecessary tool calls from the prompt using --disallowedTools and enabledPlugins: false. It would seem that these no longer work. In the end I got Claude to build a request-dumping diagnostic server, that actually measured the payload bytes, and confirmed --tools (an allowlist) is the flag that works now: 55→7 tools, 161KB→24KB, byte-verified. It also caught something specific to my workflow running the wrapper from inside an already-active

Meta's developer portal for Muse connectors: describe a connector, submit it for functional, security and legal review plus end-to-end testing, and get listed in the Muse directory.

@AloreBB

@AloreBB

Port of openai/codex-plugin-cc that lets Claude Code call Muse for reviews or delegated tasks via muse exec, with /muse:review, /muse:adversarial-review and background job commands.

@rtravellin

@rtravellin

A Claude Code plugin that adds /muse:review, /muse:critique, /muse:delegate and /muse:transfer commands, running Muse Code reviews and delegated tasks as background jobs.

Alex Volkov

@altryne

Here's my full in depth review of @muse (muse.ai) Meta's new FREE AI agent with it's own browser, computer and connectors to anything from FB marketplace to Apple health! I go into setting it up, have it buy stuff securely with @link, and 3 great usecases that

Setup. We run Qwen3.8-Flash-Next NVFP4 as our main agentic model (SGLang, RTX PRO 6000). Before its output reaches a human or gets merged, a second local model acts as judge: reviews the diff, flags real bugs only. Hosted on a 5090 32GB, so we're limited to ~30B NVFP4/GGUF class models. The metric that matters is NOT detection rate — it's false alarms on correct code. A judge that cries wolf gets ignored within a week, exactly like a flaky CI. We built our own battery: 20 injected bugs + 20 clean-but-suspicious snippets (intentional swallowed exceptions, deliberate mutability, weird-but-correct concurrency, short hashes, float patterns that look wrong). Ground-truth labeled, and a stronger model (GLM-5.2 API) arbitrates the judge's prose so scoring isn't vibes. Two passes minimum — single runs lie. Results (40 cases, temp 0, same baremo for everyone): Qwen3.8-27B NVFP4 (no-thinking) • Bugs found: 17/20 • False alarms: 3/20 • Verdict: only pass Nemotron Lightning 30B • Bugs found: 17/20 • False alarms: 0→9 across runs • Verdict: non-reproducible as judge Muse-Glimmer 30B GGUF • Bugs found: 19/20 • False alarms: 12/20 • Verdict: hypercritical Granite 4.1 30B (no-thinki

A How I AI review: the reviewer gave Muse a calendar, email and kids' chaotic schedule and got a one-shot family PDF described as more beautiful than anything made with Claude.

This week we spent about $95 trying to beat our own lineup of reviewing models. One of the candidates was Muse Spark 1.2, and it turned out to be the most interesting model in the whole test. The good, measured: • Among the best we tested at finding real problems. Scored against bugs we already knew were there, it matched our existing lineup, and it caught one real bug our lineup had missed. • Fastest model in our table. Typical answer in 18 seconds, writing at over 220 tokens a second. The speed table from our test (same job, same codebases, 33 runs per model): Model Typical time Answer length (tokens) Writing speed (tok/s) Time follows answer length Time follows question length Muse Spark 1.2 18 s 4,205 222 0.79 barely (0.08) Gemini 3.1 Pro 19 s 2,621 133 0.99 no (0.0) Gemini 3.8 Flash 22 s 1,996 89 0.91 some (0.65) GPT 5.4 29 s 3,058 105 0.95 no (below 0) Grok 4.6 38 s 2,498 61 0.61 no (below 0) Grok 4.7 44 s * 3,176 75 0.98 a little (0.30) Claude Sonnet 5 50 s * 4,471 91 0.59 barely (0.07) * Runs that finished in time only, so the real typical time is higher. The last two columns are correlations: 1 means time rises in step with that length, 0 means no lin

@alamorre

@alamorre

A Codex skill for selectively delegating bounded implementation tasks to Muse Spark 1.3 while the Astra model keeps strategic decisions and final review.

Venk Chandran

@venkchandran

I have assigned @Muse the most complex task that other agents have failed…. Appealing my San Francisco property tax bill. And it’s.. working! It informed me that tonight was the deadline to file a review and doing the appeal. Really nice work @alexandr_wang and team

A hands-on Muse review covering the Secure VM and Sentinel security model, the free, $20 and $100 tiers, and the catch.

Venelin Valkov pairs Muse Glimmer with Hermes Agent on llama.cpp for a fully free local agent, testing whether a better harness fixes the model's mixed early reviews.

@DevvGwardo

@DevvGwardo

muse-spark-crew is an opencode skill that orchestrates ten specialist subagents on Muse Spark 1.3, from architect to security auditor, with a phased fan-out and review gates.

Theo (t3.gg) tests Meta's Muse Code terminal agent powered by Muse Spark 1.2 and focuses on how cheap it is compared with Claude Code.

Digital Spaceport reviews Muse Glimmer 30B on a 4x 3090 EPYC home server, calling it weaker than Qwen 3.6 27B overall but good at one specific thing.

IchBinFabian installs Muse Code, compares Muse Spark 1.3 pricing with Claude Code and Codex, reviews the benchmarks and builds two real projects in OpenCode for a combined 82 cents.

@Telep-IO

@Telep-IO

Index of Telep IO's open-source Muse connectors, where the agent prepares a real-world action (a letter, a fax, a call) and a human reviews, pays and confirms before a provider performs it.

Taylor Arndt tested Muse with VoiceOver and found non-standard text fields and missing heading structure in chats, plus connector gaps such as iCloud email that kept it out of her work.

Invest-4-Tomorrow with Kaye

@InvestKaye

Well... that escalated quickly. 😂 Less than 24 hours after posting this, I actually gave Muse the job. “Negotiate my Verizon internet bill.” It logged into my account, reviewed the existing discounts, chatted with Verizon and negotiated: $89.99 → $69.99/month for 12 months

Function Health members can link lab results and clinician-reviewed health summaries to Muse so the agent can build personalized plans and track progress toward health goals.